{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model,discriminant_analysis,cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据方法，这里使用scikit-learn自带的糖尿病病人的数据集\n",
    "def load_data():\n",
    "    diabetes = datasets.load_diabetes()\n",
    "    return cross_validation.train_test_split(diabetes.data, diabetes.target,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ElasticNet回归模型\n",
    "def test_ElasticNet(*data):\n",
    "    train_x,test_x, train_y, test_y = data\n",
    "    regr = linear_model.ElasticNet()\n",
    "    regr.fit(train_x, train_y)\n",
    "    \n",
    "    # y=wx+b  分别显示权重及b值\n",
    "    print('【权重】coefficient:{0}'.format(regr.coef_))\n",
    "    print('【b值】intercept: {0}'.format(regr.intercept_))\n",
    "    # 均方差\n",
    "    print('【均方差】residual sum of squares:{0}'.format(np.mean((regr.predict(test_x) -test_y)**2)))\n",
    "    # 成绩\n",
    "    print('【成绩】Testing Score: {0}'.format(regr.score(test_x,test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "【权重】coefficient:[ 0.40560736  0.          3.76542456  2.38531508  0.58677945  0.22891647\n",
      " -2.15858149  2.33867566  3.49846121  1.98299707]\n",
      "【b值】intercept: 151.92763641451165\n",
      "【均方差】residual sum of squares:4922.355075721768\n",
      "【成绩】Testing Score: 0.008472003027015784\n"
     ]
    }
   ],
   "source": [
    "# 用糖尿病数据进行测试\n",
    "train_x,test_x, train_y, test_y = load_data()\n",
    "test_ElasticNet(train_x, test_x, train_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同的α ρ对结果的影响\n",
    "def test_ElasticNet_alpha_rho(*data):\n",
    "    train_x,test_x, train_y, test_y = load_data()\n",
    "    alphas = np.logspace(-2, 2)\n",
    "    rhos = np.linspace(0.01, 1)\n",
    "    test_score = []\n",
    "    for alpha in alphas:\n",
    "        for rho in rhos:\n",
    "            regr = linear_model.ElasticNet(alpha=alpha,l1_ratio=rho)\n",
    "            regr.fit(train_x,train_y)\n",
    "            test_score.append(regr.score(test_x,test_y))\n",
    "        \n",
    "    # 绘图：\n",
    "    alphas,rhos = np.meshgrid(alphas, rhos)\n",
    "    test_score = np.array(test_score).reshape(alphas.shape)\n",
    "    \n",
    "    from mpl_toolkits.mplot3d import Axes3D\n",
    "    from matplotlib import cm\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    ax.view_init(elev=10, azim=11)\n",
    "    surf = ax.plot_surface(alphas,rhos,test_score,rstride=1,cstride=1,cmap=cm.jet,linewidth=0,antialiased=False)\n",
    "    fig.colorbar(surf,shrink=0.5,aspect=5)\n",
    "\n",
    "    ax.set_xlabel(r'$\\alpha$')\n",
    "    ax.set_ylabel(r'$\\rho$')\n",
    "    ax.set_zlabel(r'score')\n",
    "    ax.set_title('ElasticNet')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用糖尿病数据进行测试\n",
    "train_x,test_x, train_y, test_y = load_data()\n",
    "test_ElasticNet_alpha_rho(train_x, test_x, train_y, test_y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
